Hong-Wei Dong, M.D. Ph. D. - US grants

[unreadable] DESCRIPTION (provided by applicant): In humans and animals even brief exposure to an odor produces long-lasting memories. In rodents, odor memories are acquired with one-trial learning and last for weeks. Although odor memories are thought to be primarily formed and/or stored in piriform cortex, data from rats suggest that certain aspects of olfactory learning and memory take place in the main olfactory bulb (MOB). Despite behavioral studies, there is virtually no information about the ability of olfactory input to modify the strength of sensory synapses in MOB. Similarly, there is scant evidence for neural substrates of learning and memory, such as long-term potentiation (LTP), in MOB. My new preliminary data demonstrate that brief, high frequency bursts (theta-burst stimulation, TBS) of olfactory nerve (ON) stimulation produce robust, long-term changes in the strength of neurotransmission at ON synapses in the glomeruli, the first site of synaptic processing in the olfactory system. This plasticity is differentially expressed by distinct MOB neuronal subtypes: (1) ON synapses with external tufted cells (ETCs) and mitral cells (MCs) exhibit LTP, and (2) ON synapses with inhibitory periglomerular cells (PGCs) exhibit long-term depression (LTD). Based on these and other new findings, I hypothesize that brief bursts of ON activity produce LTP at synapses with excitatory MOB neurons. I hypothesize that this will amplify glomerular output, via MCs, to higher order olfactory cortical structures. The LTP may involve, or be mediated by, a parallel decrease (i.e., LTD) in the excitability of local glomerular inhibitory interneurons. These hypotheses will be tested at the cellular and circuit level using patch clamp electrophysiology in rodent MOB slices. The overarching goal of this proposal is to elucidate the activity-dependant plasticity of sensory synapses in the olfactory bulb. Olfactory deficits occur with aging and several neurological disorders. The proposed research will lead to a better understanding of olfactory processing that may facilitate treatment of olfactory deficits. The overarching goal of this proposal is to elucidate activity-dependant plasticity of sensory synapses in the olfactory bulb. Activity-dependent regulation, including long-term potentiation, of sensory input to the bulb is likely to play important roles in olfactory learning and formation of odor memories. Olfactory deficits occur with aging and several neurological disorders. The proposed research will lead to a better understanding of olfactory processing that may facilitate treatment of olfactory deficits. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): This project addresses one fundamental biological question that still remains poorly understood: What is the molecular substrate of hormonal regulation in response to chronic psychological stress? Understanding this question will not only lead us into deep insight of stress and adaptation, but it will also provide potential novel drug targets for the treatment of many stress-related disorders, such as major depression and anxiety. We will address this question by using a combination of functional genomic technology and classic methods of neuroanatomy and behavioral neuroendocrinology. In Specific Aim 1, we will use a combination of retrograde tract tracing and fluorescent active cell sorting (FACS) to purify the hypothalamic neurosecretory motor neurons (HNMN), and compare their global dynamic transcriptional profiles in response to four experimental conditions: control, acute stress, chronic stress, and delayed response, in order to identify the chronic-stress-vulnerable genes. In Specific Aim 2, we will use combined genetic labeling and retrograde tract tracing methods to double-label a subpopulation of the HNMN, the CRH neuroendocrine neurons;and we will also use FACS to isolate this population and extract its RNA. Then, we will use the more sensitive and precise quantitative RT-PCR method to screen 24 candidate genes with these CRH-specific RNA samples. These candidate genes will be pre-selected from (1) the candidate gene pool generated in Specific Aim 1 and (2) the online gene expression database-the Allen Brain Atlas (ABA). Combining these two Specific Aims, we will pinpoint the cell-type specific genes involved in the regulation CRH transcription during chronic psychological stress, which will be used immediately for our future research to explore the genetic etiology of chronic stress. We will upload all data into a public accessible online database system of GEO and/or ArrayExpress. This proposal provides a novel strategy for applying functional genomics to address deep biological questions in the classic fields of behavioral neuroendocrinology, and is conceptually and technically demanding across multiple disciplines. If successful, this work could significantly advance our understanding of stress physiology and yield key insights into the endocrine response to stress associated with a number of neuropsychiatric disorders. Thus, this proposal fits into the R21 mechanism very well, which is meant for high risk but highly innovative and potentially high impact projects. The potential for a more profound understanding of the stress response, both acute and chronic, make this a very compelling proposal. To ensure the success of this project, we have established expertise in our laboratory and have developed a strong collaboration with several other local laboratories with exceptional expertise in functional genomics, genetics, and behavioral neuroendocrinology. We have also performed extensive preliminary studies and proven that all experimental procedures proposed here are feasible and reliable. PUBLIC HEALTH RELEVANCE Chronic psychological stress and mood disorders are highly prevalent within our population, and are associated with high levels of morbidity and mortality, as well as great economic cost. The lack of knowledge of the underlying molecular pathogenesis of psychological stress has prevented the exploration of more effective approaches of prevention and treatment. This proposal is a pilot project of our long-term objective that aims to determine the neural circuits and molecular mechanisms underlying chronic psychological stress. We will apply state-of-the-art FACS-array technology to determine molecular differences between acute and chronic stress, and identify hormonal regulation genes that are vulnerable to chronic psychological stress. These genes will provide candidates for exploring the precise genetic etiology of chronic stress and developing novel drug targets. Thus, this study has great value clinically and for public health.

DESCRIPTION (provided by applicant): This proposal is a continuation of our efforts to create a three-dimensional, Google Earth-like, digital Connectome atlas of the C57Black/6J mouse brain. Currently, we have established the entire pipeline for manufacturing, collecting, and processing large-scale, multi-fluorescent connectivity data in a high-throughput, industrialized manner. To track and organize these large-scale data, we have developed the Laboratory Information Management System (LIMS), which expedites data management and reduces human error. Following an unbiased, systematic, stereotaxic grid-based approach using double coinjections of proven neural circuit tracing technology, we have been constructing a skeleton Connectome map (Phase I), which will constitute about 1600 afferent and 1600 efferent pathways from 400 animals. In Phase II, proposed here, we will continuously generate large-scale, ultra-high-resolution connectivity data following a refined grid- based injection strategy using 1200 animals. The collection of these injections will generate a total of 4800 afferent and 4800 efferent pathways, enabling whole brain coverage. Further, in Phase I, we developed the revolutionary iConnectome visualization program (www.MouseConnectome.org) that features a searchable catalog of multi-fluorescent neural tracer injections available to view at high-resolution within their own bright- field Nissl-stained cytoarchitectural background. The iConnectome is accompanied by a comprehensive connectivity online database (BAMS) that enables users to map complex neuronal networks in a matrix format and to correlate them with connectivity data available for the rat, monkey, and human. In addition, the iConnectome Annotation Reporting System (iCARS), developed by our informatics team, will provide level by level annotation information for each injection site. Powerful informatics tools eventually will enable users to compare connectivity patterns of any injection site within a standard 3D anatomic frame. To do this, in Phase II, we will graphically reconstruct neural pathways that link all brain structures and will deliver four versions of the Mouse Connectome Atlas (MCA): (1) MCA1.0 (Google satellite and Google street view) for displaying high- resolution multi-fluorescent labeled raw imaging data; (2) MCA2.0 (Google Map) will provide graphically reconstructed axonal pathways and retrogradely labeled neurons; (3) MCA3.0 (Google driving direction Roadmap) will be the schematic connectivity route map that links brain structures; and (4) Connectome matrix and table-like graphic representation of neural networks generated by BAMS, which will also provide the text version navigation instructions for MCA3.0. All four versions will be synchronized and integrated into the same framework, which will enable us to assemble the ultimate version Google Earth-like Connectome atlas. Although an enormous undertaking, our Phase I progress and our team with world-class expertise in neuroanatomy, brain imaging, and neuroinformatics is a testament to our ability to deliver the completed three- dimensional digital mouse Connectome atlas in Phase III.

? DESCRIPTION (provided by applicant): In the midst of a connectomics zeitgeist, microscopy images are collected at an unprecedented rate. The amount of data collected is so overwhelming that it is a challenge for researchers to extract the organizational information of the neural networks embedded within. Developing software that provides researchers intuitive visual representations of their connections of interest would greatly aid them to generate testable hypotheses regarding the functional significance of neural networks -- the impetus for creating the mammalian connectome in the first place. The primary and initial challenge toward developing such a visualization system is the daunting task of thoroughly and reliably quantifying the enormous amount of image data. Achieving this without significant computational aid is intractable. Although algorithms for registering images and automatically reconstructing neuronal processes and axonal pathways for analysis of data exist, they are not efficient for connectivity Big Data given their protracted processing times. Utilizing the data fro the Mouse Connectome Project (MCP) at USC, we developed a beta version of Connection Lens, an innovative informatics pipeline for efficiently and expediently warping, segmenting, and quantifying connectivity data. We have successfully applied Connection Lens toward a limited set of our microscopy image data and we propose to extend its functionality to process our entire archive. Furthermore, leveraging our Connection Lens quantified data we propose to develop a complementary visualization web application. Called Projection Lens, it will render publishable visualizations of user specified connections of interest as connectivity maps, adjacency matrices, network graphs, and flatmaps. Similar to a roadmap, the program will be equipped to show all possible routes between two regions of interest and illustrate how a dysfunctioning node will affect overall information flow within the network. These features will empower researchers to quickly browse, comprehend, and publish fundamental findings regarding functionally distinct neural networks, thereby maximizing the utility of connectomics data nested in terabytes of microscopy scans. The web-based interface of Projection Lens will grant easy access to scientists world-wide. In addition, the code developed for the Connection/Projection Lens (C/PL) framework will be published freely online, and released via an open source license enabling other laboratories to quantify and visualize their data.

? DESCRIPTION (provided by applicant): In the midst of a connectomics zeitgeist, microscopy images are collected at an unprecedented rate. The amount of data collected is so overwhelming that it is a challenge for researchers to extract the organizational information of the neural networks embedded within. Developing software that provides researchers intuitive visual representations of their connections of interest would greatly aid them to generate testable hypotheses regarding the functional significance of neural networks -- the impetus for creating the mammalian connectome in the first place. The primary and initial challenge toward developing such a visualization system is the daunting task of thoroughly and reliably quantifying the enormous amount of image data. Achieving this without significant computational aid is intractable. Although algorithms for registering images and automatically reconstructing neuronal processes and axonal pathways for analysis of data exist, they are not efficient for connectivity Big Data given their protracted processing times. Utilizing the data fro the Mouse Connectome Project (MCP) at USC, we developed a beta version of Connection Lens, an innovative informatics pipeline for efficiently and expediently warping, segmenting, and quantifying connectivity data. We have successfully applied Connection Lens toward a limited set of our microscopy image data and we propose to extend its functionality to process our entire archive. Furthermore, leveraging our Connection Lens quantified data we propose to develop a complementary visualization web application. Called Projection Lens, it will render publishable visualizations of user specified connections of interest as connectivity maps, adjacency matrices, network graphs, and flatmaps. Similar to a roadmap, the program will be equipped to show all possible routes between two regions of interest and illustrate how a dysfunctioning node will affect overall information flow within the network. These features will empower researchers to quickly browse, comprehend, and publish fundamental findings regarding functionally distinct neural networks, thereby maximizing the utility of connectomics data nested in terabytes of microscopy scans. The web-based interface of Projection Lens will grant easy access to scientists world-wide. In addition, the code developed for the Connection/Projection Lens (C/PL) framework will be published freely online, and released via an open source license enabling other laboratories to quantify and visualize their data.

DESCRIPTION (provided by applicant): This proposal is a continuation of our efforts to create a three-dimensional, Google Earth-like, digital Connectome atlas of the C57Black/6J mouse brain. Currently, we have established the entire pipeline for manufacturing, collecting, and processing large-scale, multi-fluorescent connectivity data in a high-throughput, industrialized manner. To track and organize these large-scale data, we have developed the Laboratory Information Management System (LIMS), which expedites data management and reduces human error. Following an unbiased, systematic, stereotaxic grid-based approach using double coinjections of proven neural circuit tracing technology, we have been constructing a skeleton Connectome map (Phase I), which will constitute about 1600 afferent and 1600 efferent pathways from 400 animals. In Phase II, proposed here, we will continuously generate large-scale, ultra-high-resolution connectivity data following a refined grid- based injection strategy using 1200 animals. The collection of these injections will generate a total of 4800 afferent and 4800 efferent pathways, enabling whole brain coverage. Further, in Phase I, we developed the revolutionary iConnectome visualization program (www.MouseConnectome.org) that features a searchable catalog of multi-fluorescent neural tracer injections available to view at high-resolution within their own bright- field Nissl-stained cytoarchitectural background. The iConnectome is accompanied by a comprehensive connectivity online database (BAMS) that enables users to map complex neuronal networks in a matrix format and to correlate them with connectivity data available for the rat, monkey, and human. In addition, the iConnectome Annotation Reporting System (iCARS), developed by our informatics team, will provide level by level annotation information for each injection site. Powerful informatics tools eventually will enable users to compare connectivity patterns of any injection site within a standard 3D anatomic frame. To do this, in Phase II, we will graphically reconstruct neural pathways that link all brain structures and will deliver four versions of the Mouse Connectome Atlas (MCA): (1) MCA1.0 (Google satellite and Google street view) for displaying high- resolution multi-fluorescent labeled raw imaging data; (2) MCA2.0 (Google Map) will provide graphically reconstructed axonal pathways and retrogradely labeled neurons; (3) MCA3.0 (Google driving direction Roadmap) will be the schematic connectivity route map that links brain structures; and (4) Connectome matrix and table-like graphic representation of neural networks generated by BAMS, which will also provide the text version navigation instructions for MCA3.0. All four versions will be synchronized and integrated into the same framework, which will enable us to assemble the ultimate version Google Earth-like Connectome atlas. Although an enormous undertaking, our Phase I progress and our team with world-class expertise in neuroanatomy, brain imaging, and neuroinformatics is a testament to our ability to deliver the completed three- dimensional digital mouse Connectome atlas in Phase III.

PROJECT SUMMARY AND ABSTRACT A comprehensive understanding neuronal cell type diversity is an essential guide to selective manipulation and illuminating cell type specific functional contributions toward health and disease. Accordingly, the Brain Initiative Cell Census Network (BICCN) is unifying the efforts of laboratories with unique expertise in anatomy, genetics, electrophysiology, and function to classify neurons and create a common 3D atlas with integrated cell type data. To this end, our proposed collaboratory aims to anatomically characterize neuronal cell types of the mouse limbic system. Using mesoscale quadruple retrograde tracing, we will initially characterize cell types based on the anatomical location of their connectional start and end points [e.g., ACB(contralateral)?BLAa?ACB(ipsilateral)]. A two-step cre-dependent AAV tracing strategy using advanced viral tools will subsequently validate and refine specific axonal projections, collaterals, and projection fields [e.g., ACB/X/Y?BLAa?ACB/X/Y]. Injections of G-deleted rabies in CLARITY-processed tissue will label morphological features of cell types. Cre-dependent TRIO viral tracing will determine discrete inputs to each cell type, providing deeper characterization of connectivity. Novel TRIO using flp recombinase in cre- dependent mice will define projection patterns of genetically-defined cell types. Newly constructed AAV and rabies viruses tagged to spaghetti monster fluorescent proteins, applied in combination with Expansion Microscopy and multiphoton imaging, will determine the spatial organization of different synaptic inputs to the cell types. Collectively, experiments will reveal cell type anatomic location, morphology, and comprehensive connectivity. Initial efforts will focus on the limbic system, with the design extensible to neuronal characterization of the entire brain. A web-based visualization platform will be developed to enable viewing and analysis of cell type anatomy data in 2D and 3D. An online visualization tool similar in function to our iConnectome viewer will present quadruple retrograde and TRIO tracing images. Digitized, reconstructed quadruple retrograde, cre-AAV, and TRIO labeling will be placed atop the Allen Reference Atlas (ARA) to create an online 2D connectivity map, allowing easy comparison of cell type specific inputs and outputs. Common Coordinate Framework (CCF) registration and reconstruction of cre-AAV labeling experiments will provide the cell type specific 3D context of projections, with input and morphological information integrated into the viewer. An interactive, weighted and directed matrix will present an intuitive visualization of all connectivity data. 3D reconstructed neurons will also be hosted on Neuromorpho.org for interspecies comparison. Our current informatics pipelines will be extended and optimized to support the proposed viewer features. We expect our technologies to elucidate diverse cell type specific networks and provide foundations for the overarching goal of the BICCN of creating a comprehensive 3D cell type atlas.

Project Summary/Abstract Leveraging existing informatics platforms from the Mouse Connectome Project at USC (www.MouseConnectome.org) and from the Brainome Epigenomics Project at Salk/UCSD (http://brainome.ucsd.edu/CEMBA/), the CEMBA Data Core aims to develop innovative informatics tools for the collection, analysis, visualization, modeling, storage, and distribution of two major categories of cell-type information: molecular signatures (Research Segment 1) and anatomy (Research Segment 2). A scalable bioinformatics pipeline will be developed to collect, manage, analyze and visualize brain region-specific, whole- genome DNA methylation and chromatin accessibility (ATAC-Seq) within the comprehensive context of the mouse brain, achieving integration of cell type-specific molecular data with anatomical data. In parallel, the CEMBA Data Core will develop a dynamic, web-based informatics platform that facilitates the collection, analysis, and visualization of cell-type specific connectivity data. Further, the use of novel informatics tools will enable synchronization of cell type-specific epigenetic networks with neural networks. Finally, all of these data will be presented through intuitive online visualization tools, allowing users to view and analyze the information within the Allen Reference Atlas neuroanatomy framework. The CEMBA-DC will be the primary point of contact within the broader BICCN, including the Brain Cell Data Center (BCDC) U24 program. The CEMBA Data Core will work together with the BCDC and other investigators to harmonize nomenclature and semantic structures.

PROJECT SUMMARY/ABSTRACT Identifying the diversity of cell types in the nervous system will allow for their selective manipulation and reveal their functional contributions in health and disease. However, this is not a trivial undertaking and is hindered by the lack of consensus on which properties to use for classification. Characteristics like anatomical location, connectivity, morphology, molecular profile, and electrophysiological properties have been used as classification systems, but singly, none provide a combined view of all these characteristics. To address this, we propose a multidisciplinary approach that will provide all of this information for each cell type of the mouse hippocampus and subiculum (HPF/SUB). We recently identified all HPF/SUB molecular domains and assembled their connectivity networks using tracing data from the Mouse Connectome Project (www.MouseConnectome.org). Our multiple retrograde tracer injections revealed that the HPF/SUB contain multiple intermixed populations of cells with unique projection targets, suggesting different cell types that could be defined based on their connectional start and end points with anatomic specificity. Therefore, here, we propose to use a quadruple retrograde tracing method to initially classify these neurons based on these connections. Subsequently, a two- step cre-dependent AAV tracing method will determine all outputs of each cell type. To determine their molecular identities, seqFISH, with preselected hippocampal marker genes, will be performed on the tissue from the quadruple retrograde tracing data. Importantly, seqFISH preserves spatial information so that the precise anatomic locations of the tracer-labeled cells and the genes will be retained. Next, rabies injections placed in targets of each HPF/SUB cell type will reveal their morphology. CLARITY and two-photon microscopy will enable morphological assessment in 3D and neuronal reconstructions for further analysis. To examine electrophysiologcial properties, each cell type will be labeled with retrograde tracers for identification purposes and ex vivo cell patch clamping will be performed on the labeled cells. Finally, cre-dependent viral tracing (TRIO) will determine inputs to the different HPF/SUB cells types. With the aid of Expansion Microscopy and two-photon imaging, a combined anterograde/rabies tracing strategy will show precise locations of select inputs to cell types. If successful, this project can be applied to characterize neuronal cell types of the entire brain. All data will be publicly shared. Images from the quadruple retrograde, two-step cre-AAV, and TRIO tracing experiments will be available in the iConnectome Cell Type Viewer. Graphic reconstructions of labeling from these experiments will be compiled and presented within a common neuroanatomic frame through a Cell Type Connectivity Map. The iConnectome Cell Type Morphology Viewer will showcase labeling from the double rabies experiments and provide details like 3D reconstructions and their morphological and electrophysiological properties. Cell type connections will be visualized in an interactive Web Connectivity Matrix. Our in-house informatics pipelines and algorithms will be further developed and optimized to support the proposed features of all viewers. 

PROJECT SUMMARY/ABSTRACT The objective of our Mouse Connectome Project at USC (MCP) is to chart the long-range connectivity of ~800 delineated structures of the mouse brain in an effort to reveal its network organization. In Phase I (2009-2010), we established an efficient data production, collection, and image processing workflow dedicated to compiling connectomics data of the highest quality. We adopted an injection strategy that produced data most conducive for network analysis by simultaneously revealing, for any brain region (i.e. A), its (1) inputs (A?B); (2) outputs (A?B); (3) reciprocal or recurrent connections (A?B); and (4) intermediate stations, which bridge brain structures that are not directly connected (A?C?B). In Phase II (2011-2016), we traced ~2000 pathways from injections placed across the entire cerebral hemisphere and thalamus. As proposed, in Phase III (2017-2022) we will collect and analyze connections data for the hypothalamus, midbrain, pons, medulla, and cerebellum (~1400 additional pathways) (Specific Aim 1). Combined, these pathways will be used to construct the most comprehensive mesoscale connectome that charts all point-to-point connections of the entire mouse brain. Compiling these connectivity data sets however is only the first step in constructing the connectome. The ensuing challenge is to analyze the enormous data to extract information regarding network organization. Based on graph theoretical analysis of 600 manually annotated pathways, we assembled the global networks of the mammalian neocortex (Zingg et al., Cell, 2014). Although the gold standard, manual analysis was laborious, time consuming, and not efficient for our ultimate goal of generating brain-wide connectivity maps and networks. Therefore, in Phase II, we designed and created an innovative informatics workflow that efficiently and reliably registers, reconstructs, and annotates large-scale connections data. This workflow will be applied in Phase III to accelerate image processing, creation of connectivity maps, data annotation, and analysis. In Phase III, we will also initiate the first stage of constructing cell type specific neural networks (Specific Aim 2). Our connectivity-based cell type classification strategy will be used to identify all cell types of the medial prefrontal cortex and to gain a census of each cell type using 2D and 3D images. Novel rabies viral tracing will be employed to systematically reveal the neuronal inputs to these distinct cell populations. All of our data will be available as open resources (www.MouseConnctome.org) (Specific Aim 3): (1) the iConnectome viewer is the only visualization tool that allows users to view images of multiple fluorescently-labeled pathways within their own bright-field Nissl background and corresponding level of a standard mouse brain atlas; (2) the iConnectome Map Viewer allows access to connectivity maps, which feature hundreds of reconstructed pathways compiled atop a neuroanatomic frame; (3) the iConnectome Cell Type Viewer, which will feature images of all cell type circuits; (4) the Cell Type Map Viewer will host cell type specific connectivity maps; (5) the online Web Connectivity Matrix will present connections in a matrix; and (6) our 3D viewer will provide an overview of all connections in 3D.

PROJECT SUMMARY Although great efforts have been dedicated to characterizing neuronal cell types in the brain, systematic studies on the brain-spinal cord connectome and associated spinal neuronal types are lacking. In this project, a team of seven laboratories proposes to use a highly innovative and multidisciplinary approach to systematically characterize neuronal types in the spinal cord based on their anatomy, connectivity, neuronal morphologies, molecular identities, and electrophysiological properties. In Aim 1, we will use a newly developed AAV anterograde transsynaptic tagging method to label spinal cord neurons that receive descending inputs from different brain regions, and use a retrograde viral tracer, AAVretro, to label spinal neurons that project to defined brain regions. These tagged neurons will be imaged in the intact whole spinal cord with a newly developed fast 3D light sheet microscopy technique, and targeted for recording in slice preparations. The axonal collateral patterns, dendritic morphologies, and electrophysiological properties will be compared between different input/output-defined spinal neuron groups. In Aim 2, the gene expression patterns of the tagged neurons will be determined in situ by sequential bar-coded FISH (seqFISH), with candidate marker genes obtained from online resources, or from single-cell sorting and RNA sequencing (Dropseq). In Aim 3, all collected data on connectivity, anatomical cell type distribution map, neuronal morphologies, molecular identities, and electrophysiological properties will be used for classifying spinal neuron types connected with brain, and an open-source data portal will be established which will allow users to search, view, and analyze the multi-modal and integrative cell-type specific data. Together, we aim to construct a comprehensive cell- type atlas of the mouse brain-spinal cord connectome.

PROJECT SUMMARY Integrating molecular, morphological, and connectomic properties is critical for unbiased classification of neuronal cell types in the mammalian brain. Here we propose a novel approach to classify neuronal cell types by brainwide comprehensive profiling of the dendritic morphology of genetically-defined neurons in the mouse brain. We have developed an innovative mouse genetic tool, called Mosaicism with Repeat Frameshift (or MORF), which enables sparsely and stochastically labeling of genetically-defined neurons in mice. MORF reporter mice can label in exquisite detail single neurons from dendrite and spines to axons and axonal terminals at a labeling frequency of 1-5% of a given neuronal population. We propose to cross our new MORF lines with Cre mouse lines for striatal medium spiny neurons (MSNs) of direct- and indirect pathways, and for cortical pyramidal neurons of distinct cortical layers (i.e. L2/3/4, L5 and L6). Each MORF/Cre mouse will allow us to image the detailed dendritic morphology for thousands of genetically-defined striatal and cortical neurons (i.e. dendritome). We have also developed and streamlined imaging and computational tools to acquire and register brainwide single neuron morphological data onto a standard reference mouse brain atlas. We will digitally reconstruct hundreds of thousands of MORF-labeled neurons using our novel program called G-Cut. Reconstructed neurons will subsequently used for morphology based clustering to define new morphological subtypes, which in turn can be analyzed for the expression of novel molecular markers neuronal cell types (e.g. from single cell RNA-sequencing). Finally, we will disseminate the data to the Brain Cell Data Center (BCDC) for data integration with those from other BRAIN Initiative Cell Census Network (BICCN) and for data access by the broader neuroscience research community. In addition to dendritome data generation and analyses, we will further advance our MORF method by generating new MORF reporter mouse lines with logarithmic fold decrease in the Cre- dependent labeling frequencies, which will permit imaging of the complete, brainwide morphology of genetically-defined single neurons that include both dendritic and axonal arborization. Such tool should greatly facilitate the neuronal morphology based cell type classification. Finally, we will develop integrated computer hardware and software for domain-specific computing for automated image processing and neuronal reconstruction, a major bottleneck in analyzing large bioimage datasets. Altogether we will provide rich dendritome information to enable unbiased, morphology-based neuronal cell type classification, and novel mouse genetic tools and computer software and hardware to advance the field of large-scale neuronal morphological imaging and analyses for the comprehensive study of the mammalian brain.

PROJECT SUMMARY A major challenge in studying the mammalian brain is to characterize the integrative properties of individual neurons, such as molecular profiles, complete morphology (dendrites, axons, synapses), connectivity, and activity; furthermore, this must be done at a scale that is commensurate with the goal of understanding all the neurons and their circuitry in the brain. While current single-cell transcriptomic and epigenomic profiling techniques are highly quantitative, scalable and informative, the technologies to study other neuronal cell-type defining properties(e.g. single-neuron brain-wide morphology and synaptic connectivity) are low throughput, labor intensive, poorly scalable and often yield partial data. Emerging neuronal cell type classification studies in invertebrates (e.g. Drosophila) and in rodents suggest that the neuronal morphological data such as axonal projection patterns are correlated, but may also be independent to the cell classes defined by single-cell gene expression. Thus, a complete and unbiased survey of mammalian neuronal cell census should include orthogonal data types consisting of both molecular profiles and brainwide morphology of single neurons. Finally, for emerging new cell types defined by unique transcriptomic profiles, the causal links between the cell-type-defining ?neuronal identity? genes and other cell-type-specific features, such as morphology, synaptic connectivity and activity, remain elusive and cannot be readily characterized in a scalable manner. In this proposal (in response to RFA MH-21-140), we will address these challenges by building upon a novel neurotechnology called Mosaicism with Repeat Frameshift, or MORF. MORF mice can confer cell- type specific, sparse and brightly labeling of neurons and glia to illuminate their complete morphologies in the mouse brain. The innovative aspect of the MORF mice is the use of an out-of-frame mononucleotide repeat as a stochastic translational switch; and its random frameshift leads to the expression of an extremely bright membrane-bound immunoreporter protein in 1-5% of genetically-defined neurons. In this proposal, we will generate four next-generation MORF mouse models that will allow: (1). precise and sparse labeling of neuronal cell types based on two genetic drivers (i.e. two molecular markers that define the neuronal cell type); (2). Cre-dependent labeling of endogenous presynaptic proteins in sparsely labeled GABAergic and cortical glutamatergic neurons; (3). selective expression of genome-editing tools in genetically and sparsely labeled neurons to support perturbation and multiplex subcellular labeling; and (4). development of an innovative and integrative multiscale imaging and registration pipeline to provide proof-of-concept data that analyzes brainwide morphology and connectivity of genetically-defined single neurons. Together, our grant may help to develop generalizable, scalable and democratizable tools to advance the study of neuronal morphology, synapses and connectivity, and genetic perturbation. These tools will facilitate the construction of mammalian brain cell census and advance the study of brain development, function and disease at the resolution of single neurons.